Investigating Collaborative Data Practices: a Case Study on Artificial Intelligence for Healthcare Research
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916093147217920 |
|---|---|
| author | Henkin, Rafael Remfry, Elizabeth Reynolds, Duncan J. Clinch, Megan Barnes, Michael R. |
| author_facet | Henkin, Rafael Remfry, Elizabeth Reynolds, Duncan J. Clinch, Megan Barnes, Michael R. |
| contents | Developing artificial intelligence (AI) tools for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other disciplines together. In this paper, we explore the collaborative data practices of research consortia tasked with applying AI tools to understand and manage multiple long-term conditions in the UK. Through an inductive thematic analysis of 13 semi-structured interviews with participants of these consortia, we aimed to understand how collaboration happens based on the tools used, communication processes and settings, as well as the conditions and obstacles for collaborative work. Our findings reveal the adaptation of tools that are used for sharing knowledge and the tailoring of information based on the audience, particularly those from a clinical or patient perspective. Limitations on the ability to do this were also found to be imposed by the use of electronic healthcare records and access to datasets. We identified meetings as the key setting for facilitating exchanges between disciplines and allowing for the blending and creation of knowledge. Finally, we bring to light the conditions needed to facilitate collaboration and discuss how some of the challenges may be navigated in future work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18424 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Investigating Collaborative Data Practices: a Case Study on Artificial Intelligence for Healthcare Research Henkin, Rafael Remfry, Elizabeth Reynolds, Duncan J. Clinch, Megan Barnes, Michael R. Human-Computer Interaction Artificial Intelligence Computers and Society Developing artificial intelligence (AI) tools for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other disciplines together. In this paper, we explore the collaborative data practices of research consortia tasked with applying AI tools to understand and manage multiple long-term conditions in the UK. Through an inductive thematic analysis of 13 semi-structured interviews with participants of these consortia, we aimed to understand how collaboration happens based on the tools used, communication processes and settings, as well as the conditions and obstacles for collaborative work. Our findings reveal the adaptation of tools that are used for sharing knowledge and the tailoring of information based on the audience, particularly those from a clinical or patient perspective. Limitations on the ability to do this were also found to be imposed by the use of electronic healthcare records and access to datasets. We identified meetings as the key setting for facilitating exchanges between disciplines and allowing for the blending and creation of knowledge. Finally, we bring to light the conditions needed to facilitate collaboration and discuss how some of the challenges may be navigated in future work. |
| title | Investigating Collaborative Data Practices: a Case Study on Artificial Intelligence for Healthcare Research |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2311.18424 |